Recommendation system is a very common and important component of e-commerce platforms. As well known, recommendation systems (sometimes replacing “system” with a synonym such as platform or engine) belong to a subclass of information filtering systems that seek to predict the rating or preference that a user wants give to an item/product. The recommendation system functions to select and then recommend items to the user. Few examples of the items include consumer products (e.g., books, personal computers, or other consumer goods), entertainment content (e.g., music, movies, and TV programs), news stories, web pages, publications, services, applications or the like. To this end, the recommendation system may use filtering techniques that attempt to enable the system to select items that are likely to be of interest to the user. Sometimes, the recommendation system compares a user's profile to some reference characteristics and seeks to predict a rating that the user may want to give to an item the user has not yet rated. These characteristics may be based on the information item (content-based approach) or the user's social environment (collaborative filtering approach). Such recommendation systems are now commonly used in retail arena.
Current trends in the retail arena focus heavily on the users/consumers shopping experience. Retailers compete in the market for expanding their consumer base and this competition is primarily achieved by providing competitive prices, deals and discounts to their users. This information can often be overwhelming for the users and may sometimes become difficult for him or her to select a set of items.
While some work in the domain exists for providing recommendations to the users in order to assist them while shopping. Most recommendation systems today take into account the users shopping history and profile details when making recommendations. Mostly the efforts are centered on online retail outlets and further not much attention is paid to personalization and assistance to the users with respect to physical stores. In fact, physical stores are also finding it difficult to retain their users/customers due to the ever increasing competition from the online stores. One of the key reasons is the lack of personalized shopping experience offered by the physical stores. In view of the foregoing described needs, the present disclosure provides methods and systems for enhancing shopping experience of users in physical stores.